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DDoS attack detection using MLP and Random Forest Algorithms

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Abstract

Distributed Denial of Service (DDoS) attacks continue to be the most dangerous over the Internet. With the rapid advancement of information and communication technology, the consequences of a DDoS attack are becoming increasingly devastating. As a result, DDoS attack detection research is now becoming significantly important. In this paper, we employed different types of machine learning techniques for the detection of DDoS attack packets and their types. Random Forest (RF), multi-layer perceptrons (MLP), Support Vector Machine and K-Nearest Neighbor are used in our work and the methods have presented promising results. RF showed an accuracy of 99.13% on both train and validation data and 97% on full test data. On the other hand, the MLP showed an accuracy of 97.96% on train data and 98.53% on validation data and 74% on full test dataset.

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  1. Department of Computer Science, Central University of Kerala, Kasargod, Kerala, 671316, India

    Ashfaq Ahmad Najar & S. Manohar Naik

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  1. Ashfaq Ahmad Najar
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  2. S. Manohar Naik
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Correspondence to Ashfaq Ahmad Najar.

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Najar, A.A., Manohar Naik, S. DDoS attack detection using MLP and Random Forest Algorithms. Int. j. inf. tecnol. 14, 2317–2327 (2022). https://doi.org/10.1007/s41870-022-01003-x

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